Method and system for predicting failures in diverse set of asset types in an enterprise

ABSTRACT

Disclosed herein is a method and a failure prediction system for predicting failures in a diverse set of asset types in an enterprise. In an embodiment, asset information related each assets are analyzed for determining an asset type and a failure mode of each of the assets. Thereafter, one of a plurality of prediction models is selected for predicting failures in each of the assets based on the asset type and the failure mode of each of the assets. Finally, selected one of the plurality of prediction models is used to analyze the asset information for predicting the failures in each of the assets. In an embodiment, the present disclosure provides a universal failure prediction system for predicting failures in the diverse set of asset types and thereby eliminates requirement of using multiple prediction systems for predicting failures in each type of the assets.

This application claims the benefit of Indian Patent Application SerialNo. 201941003214 filed Jan. 25, 2019, which is hereby incorporated byreference in its entirety.

FIELD

The present subject matter is, in general, related to enterprise assetmanagement and more particularly, but not exclusively, to a method andsystem for predicting failures in a diverse set of asset types in anenterprise.

BACKGROUND

Presently, there is an advent of shift from reactive asset maintenanceto proactive asset-failure prediction in an enterprise. There arenumerous asset-failure prediction solutions currently available in theindustry. These prediction solutions obtain information about workingcondition and life of an asset and perform various analysis includingreliability centric maintenance, condition-based monitoring andpredictive maintenance. Further, the prediction solutions also estimateoperational costs, remaining useful life and safety factors of theasset. However, the existing prediction solutions are very specific toasset types and are designed to analyze and predict failures in theassets belonging to a particular asset type.

Typically, attributes that define the operating conditions will bedifferent for different types of assets. Similarly, the failure patternsand modes of failure will also be different for the different type ofassets. Moreover, in large organizations, different individual assets,even when they belong to the same type, would have been sourced fromdifferent manufacturers. Consequently, these assets may generate data indifferent forms as they have different specifications andmanufacturer-specific configuration. In all the above scenarios,different data interpretation, different processing logic and differentstatistical models will be required for predicting the failures in eachtype of assets. This requires the organizations to develop, deploy,operate and maintain different systems and applications to predictfailures in different types of assets.

However, in large organizations, costs and efforts required for settingup these many failure prediction systems are multiplied by many folds.For example, a large water utility provider having thousands of assetslike reservoir, pipeline, pumps, turbines and valves, must deploy andmaintain a large number of failure prediction systems, with huge costsinvolved. This multiplicity of failure prediction systems is thus amajor technical shortcoming in the current state of the art with respectto proactive asset-failure prediction systems in large enterprises.Therefore, there is a need for a failure prediction system which canpredict failures in the assets, irrespective of the type of the assets.

The information disclosed in this background of the disclosure sectionis only for enhancement of understanding of the general background ofthe invention and should not be taken as an acknowledgement or any formof suggestion that this information forms the prior art already known toa person skilled in the art.

SUMMARY

Disclosed herein is a method of predicting failures in a diverse set ofasset types in an enterprise. The method comprises receiving, by afailure prediction system, asset information related to one or moreassets from one or more data sources associated with the one or moreassets. The one or more assets belong to one or more asset types.Subsequent to receiving the asset information, the method comprisesdetermining an asset type and a failure mode of each of the one or moreassets based on analysis of the asset information. Thereafter, themethod comprises selecting one of a plurality of prediction models forpredicting failures in each of the one or more assets based on the assettype and the failure mode of each of the one or more assets. Finally,the method comprises analyzing the asset information using identifiedone of the plurality of prediction models for predicting the failures ineach of the one or more assets.

Further, the present disclosure relates to a failure prediction systemfor predicting failures in a diverse set of asset types in anenterprise. The failure prediction system comprises a processor and amemory. The memory is communicatively coupled to the processor andstores processor-executable instructions, which on execution, cause theprocessor to receive asset information related to one or more assetsfrom one or more data sources associated with the one or more assets.The one or more assets belong to one or more asset types. Further, theinstructions cause the processor to determine an asset type and afailure mode of each of the one or more assets based on analysis of theasset information. Thereafter, the instructions cause the processor toselect one of a plurality of prediction models to predict failures ineach of the one or more assets based on the asset type and the failuremode of each of the one or more assets. Finally, the instructions causethe processor to analyze the asset information using identified one ofthe plurality of prediction models to predict the failures in each ofthe one or more assets.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate exemplary embodiments and, togetherwith the description, explain the disclosed principles. In the figures,the left-most digit(s) of a reference number identifies the figure inwhich the reference number first appears. The same numbers are usedthroughout the figures to reference like features and components. Someembodiments of system and/or methods in accordance with embodiments ofthe present subject matter are now described, by way of example only,and regarding the accompanying figures, in which:

FIG. 1 illustrates an exemplary environment for predicting failures in adiverse set of asset types in an enterprise in accordance with someembodiments of the present disclosure;

FIG. 2 shows a detailed block diagram illustrating a failure predictionsystem in accordance with some embodiments of the present disclosure;

FIG. 3A shows a flowchart illustrating a method for checking sufficiencyof asset information in accordance with some embodiments of the presentdisclosure;

FIG. 3B shows a flowchart illustrating a method of selecting a failureprediction model in accordance with some embodiments of the presentdisclosure;

FIG. 3C shows a flowchart illustrating a method of managing the failureprediction models in accordance with some embodiments of the presentdisclosure;

FIG. 4 shows a flowchart illustrating a method of predicting failures ina diverse set of asset types in an enterprise in accordance with someembodiments of the present disclosure; and

FIG. 5 illustrates a block diagram of an exemplary computer system forimplementing embodiments consistent with the present disclosure.

It should be appreciated by those skilled in the art that any blockdiagrams herein represent conceptual views of illustrative systemsembodying the principles of the present subject matter. Similarly, itwill be appreciated that any flow charts, flow diagrams, statetransition diagrams, pseudo code, and the like represent variousprocesses which may be substantially represented in computer readablemedium and executed by a computer or processor, whether such computer orprocessor is explicitly shown.

DETAILED DESCRIPTION

In the present document, the word “exemplary” is used herein to mean“serving as an example, instance, or illustration.” Any embodiment orimplementation of the present subject matter described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments.

While the disclosure is susceptible to various modifications andalternative forms, specific embodiment thereof has been shown by way ofexample in the drawings and will be described in detail below. It shouldbe understood, however that it is not intended to limit the disclosureto the specific forms disclosed, but on the contrary, the disclosure isto cover all modifications, equivalents, and alternative falling withinthe scope of the disclosure.

The terms “comprises”, “comprising”, “includes”, or any other variationsthereof, are intended to cover a non-exclusive inclusion, such that asetup, device, or method that comprises a list of components or stepsdoes not include only those components or steps but may include othercomponents or steps not expressly listed or inherent to such setup ordevice or method. In other words, one or more elements in a system orapparatus proceeded by “comprises . . . a” does not, without moreconstraints, preclude the existence of other elements or additionalelements in the system or method.

The present disclosure relates to a method and a failure predictionsystem for predicting failures in a diverse set of asset types in anenterprise. That is, the failure prediction system may be treated as asingle generalized system that can predict failures on varied set ofasset types in the enterprise. In an embodiment, various input variableslike sensor readings and events like failure alarms are collected fromthe individual assets and are indexed based on a unique identifiercorresponding to each of the assets. Further, the collected sensorreadings and events information are segregated based on the assetidentifier to build or select specific predictive models based on theasset type. Models in the model repository are refreshed periodically.Also, the failure prediction system may generate predictions fornecessary preventive maintenance based on near real-time events receivedfrom that asset. Subsequently, a modelling approach is used to buildvarious failure prediction models for predicting failures in each of theasset tapes. In an embodiment, each of the failure prediction models aretrained with the metadata corresponding to each type of the assets.

In the following detailed description of the embodiments of thedisclosure, reference is made to the accompanying drawings that form apart hereof, and in which are shown by way of illustration specificembodiments in which the disclosure may be practiced. These embodimentsare described in sufficient detail to enable those skilled in the art topractice the disclosure, and it is to be understood that otherembodiments may be utilized and that changes may be made withoutdeparting from the scope of the present disclosure. The followingdescription is, therefore, not to be taken in a limiting sense.

FIG. 1 illustrates an exemplary environment for predicting failures in adiverse set of asset types in an enterprise in accordance with someembodiments of the present disclosure.

In some implementations, the environment 100 may include one or moreassets 101 of an enterprise, one or more data sources 103 associatedwith the one or more assets 101 and a failure prediction system 105. Theone or more assets 101 may include various hardware resources being usedin the enterprise. As an example, in an enterprise that manages watersupply, the one or more assets 101 may include, without limiting, awater reservoir, pressure pumps, pipes, supply meters and the like. Inan embodiment, the one or more assets 101 may belong to a diverse set ofasset types.

In an embodiment, the one or more data sources 103 associated with theone or more assets 101 may include, without limiting to, one or moresensors configured with the one or more assets 101 and data logsindicating operation, maintenance and servicing of the one or moreassets 101. As an example, the one or more sensors may include, withoutlimitation, temperature sensors, pressure sensors, emergency eventindicators and alarms. The information collected by the one or moresensors may be used to determine an operational state of the one or moreassets 101 at any required point of time. Further, the data logs mayinclude, without limitation, operational data collected by monitoringusage and behaviour of the one or more assets 101 over a period of time.

In an embodiment, the failure prediction system 105 may be any computingdevice including, but not limited to, a desktop computer, a laptop or asmartphone, which may be configured with a plurality of processingmodels for predicting failures in the one or more assets 101. In someimplementations, the failure prediction system 105 may be deployedwithin the enterprise and locally connected to the one or more datasources 103 for receiving asset information related to the one or moreassets 101. In alternative implementations, the failure predictionsystem 105 may be deployed as a centralized server and communicativelyconnected with the one or more data sources 103 for receiving the assetinformation.

In an embodiment, the failure prediction system 105 may receive theasset information in real-time or at predetermined intervals. As anexample, the asset information may be automatically retrieved from theone or more data sources 103 at each interval of 1 hour. Further, theasset information may include, without limiting to, an asset identifiercorresponding to each of the one or more assets 101, values of operatingparameters of the one or more assets 101, values of operating parametersof an ambient environment of the one or more assets 101, eventsindicating changes in operational states of the one or more assets 101and alarms indicating variations in operation of the one or more assets101. In an embodiment, subsequent to receiving the asset information,the failure prediction system 105 may perform one or more data cleansingoperations on the asset information for eliminating one or moreirregularities in the asset information. As an example, the one or moreirregularities may include, without limiting to, redundancy ininformation, incomplete information, pseudo and/or incorrectobservations and the like. After performing the one or more datacleansing operations, the asset information may be analysed to determinesufficiency of the asset information based on comparison of the assetinformation with one or more predetermined asset parameters.

In an embodiment, the failure prediction system 105 performs datasufficiency check by determining an asset type from the assetinformation and a failure mode of each of the one or more assets 101.The asset type may indicate a group and/or an asset-class to which theone or more assets 101 may belong. As an example, the asset type mayinclude, without limiting to, asset categories such as largemachineries, movable equipment, small tools or end-user products.Further, the failure mode of each of the one or more assets 101 may beindicate a cause and/or an effect of a failure in the one or more assets101. As an example, the failure mode may include without limiting to,premature termination of operation, failure during the operation,failure to terminate operation within a prescribed time, degraded orexcessive operational capability of the asset and the like.

In an embodiment, upon determining the asset type and the failure modeof each of the one or more assets 101, the failure prediction system 105may select one of a plurality of prediction models 107 for predictingfailures in each of the one or more assets 101. In an embodiment, theplurality of prediction models 107 may be pre-trained learning models,which are trained with information related to various asset types andfailure modes of the one or more assets 101. In an implementation, eachof the plurality of prediction models 107 may be trained to predictfailures in one of the various asset types. That is, the selection ofone of the plurality of prediction models 107 for predicting thefailures may be based on the asset type and the failure mode of the oneor more assets 101. Therefore, selection of the prediction models 107includes comparing the asset type and the failure mode of each of theone or more assets 101 with pretrained asset type and pretrained failuremode used for training each of the plurality of prediction models 107.In an implementation, each of the plurality of prediction models 107 maybe stored in a model repository associated with the failure predictionsystem 105.

In an embodiment, upon selecting one of the plurality of predictionmodels 107 for predicting the failures, the failure prediction system105 may analyse the asset information using selected one of theplurality of prediction models 107 for predicting the failures in eachof the one or more assets 101. Here, the selected one of the pluralityof prediction models 107 may compare the asset information of the one ormore assets 101 with corresponding asset information used for trainingthe selected one of the plurality prediction models 107, for identifyingfailures in the one or more assets 101. However, if the asset type andthe failure mode of the one or more assets 101 do not match with thepretrained asset type and the pretrained failure mode of any of theplurality of prediction models 107, then the failure prediction system105 may dynamically create a new prediction models 107 for predictingfailures in the one or more assets 101. Further, the newly createdprediction models 107 may be stored in the model repository and used forpredicting failures in the one or more assets 101 in future instances.

In an embodiment, the failure prediction system 105 may performadditional training of the plurality of prediction models 107 in themodel repository when prediction accuracy level of the plurality ofprediction models 107 is less than a predetermined threshold. Asexample, the predetermined threshold may be 90% accuracy. That is, whenthe accuracy of predictions made by the plurality of prediction models107 is less than 90%, the plurality of prediction models 107 may betrained with relevant asset information for enhancing accuracy of theplurality of prediction models 107.

In an embodiment, upon predicting the failures in the one or more assets101, the failure prediction system 105 may generate one or morenotification events that include information related to the predictedfailures. Further, the failure prediction system 105 may transmit theone or more notification events to predetermined asset managementpersonnel such as asset managers, asset maintenance and servicesupervisors, field technicians and the like. Thus, the failureprediction system 105 proactively predicts the failures in the one ormore assets 101 and helps in preventing occurrence of the predictedfailures.

FIG. 2 shows a detailed block diagram illustrating a failure predictionsystem 105 in accordance with some embodiments of the presentdisclosure.

In some implementations, the failure prediction system 105 may includean I/O interface 201, a processor 203, and a memory 205. The I/Ointerface 201 may be configured to receive asset information 211 relatedto one or more assets 101 from one or more data sources 103 associatedwith the one or more assets 101. The memory 205 may be communicativelycoupled to the processor 203 and may store data 207 and one or moremodules 209. The processor 203 may be configured to perform one or morefunctions of the failure prediction system 105 for predicting failuresin a diverse set of asset types, using the data 207 and the one or moremodules 209.

In an embodiment, the data 207 may include, without limitation, assetinformation 211, asset metadata 213 and other data 217. In someimplementations, the data 207 may be stored within the memory 205 in theform of various data structures. Additionally, the data 207 may beorganized using data models, such as relational or hierarchical datamodels. The other data 217 may store various temporary data and filesgenerated by one or more modules 209 while performing various functionsof the failure prediction system 105. As an example, the other data 217may also include an asset identifier and a model repository associatedwith the failure prediction system 105.

In an embodiment, the asset information 211 may include all theinformation related to the one or more assets 101 in the enterprise. Theasset information 211 may be received from the one or more data sources103 associated with the one or more assets 101. As an example, the assetinformation 211 may include, without limiting to, an asset identifiercorresponding to each of the one or more assets 101, values of operatingparameters of the one or more assets 101, values of operating parametersof an ambient environment of the one or more assets 101, eventsindicating changes in operational states of the one or more assets 101and alarms indicating variations in operation of the one or more assets101. Here, the asset identifier may be a unique identity of the assetwhich may be to identify the location, type and operational role of theasset in the enterprise. Operating parameters of the assets 101 mayinclude parameters such as up-time of the asset, age of the asset,failure history of the asset, ideal operating conditions for properfunctioning of the asset and the like. Operating parameters of theambient environment may be external parameters that affect functioningof the asset and may include, without limiting to, ambient temperature,surrounding pressure and the like. In an embodiment, the assetinformation 211 may be received in various forms including, withoutlimiting to, alarms, signals and event notifications.

In an embodiment, the asset metadata 213 may include, without limitingto, list of asset identifiers of each of the one or more assets 101 inthe enterprise, the asset type of each of the one or more assets 101,corresponding failure modes, operational parameters or attributes, eventoccurrence details of each of the one or more assets 101. The assetmetadata 213 may be used for determining the asset type and the failuremode of the one or more assets 101 based on the asset identifier of theone or more assets 101. Further, whenever new assets 101 have beendeployed in the enterprise, the asset metadata 213 may be updated toinclude asset identifiers of the newly deployed assets 101.

In an embodiment, each of the data 207 may be processed by the one ormore modules 209. In some implementations, the one or more modules 209may be communicatively coupled to the processor 203 for performing oneor more functions of the failure prediction system 105. In animplementation, the one or more modules 209 may include, withoutlimiting to, a receiving module 219, an asset type determination module221, a failure mode determination module 223, a model selection module225, plurality of prediction models 107 and other modules 229.

As used herein, the term module refers to an Application SpecificIntegrated Circuit (ASIC), an electronic circuit, a processor (shared,dedicated, or group) and memory that execute one or more software orfirmware programs, a combinational logic circuit, and/or other suitablecomponents that provide the described functionality. In an embodiment,the other modules 229 may be used to perform various miscellaneousfunctionalities of the failure prediction system 105. It will beappreciated that such one or more modules 209 may be represented as asingle module or a combination of different modules.

In an embodiment, the receiving module 219 may be configured forreceiving the asset information 211 related to the one or more assets101 from the one or more data sources 103 associated with the one ormore assets 101. The receiving module 219 may receive the assetinformation 211 directly from the one or more data sources 103 or mayimport the asset information 211 from an intermediate storage associatedwith the failure prediction system 105. That is, the asset information211 may be streamed in real-time or ingested periodically in batches. Insome embodiments, the one or more data sources 103 may deliver the assetinformation 211 continuously into a common data stream for ensuring thereal-time data ingestion. The real-time data streaming/ingestion processmay support continuous collection of the asset information 211 usingstreaming components like message queues. In alternative embodiments,the asset information 211 from the one or more data sources 103 may becollected at periodic frequencies into files and provided for ingestionin batches. The batch ingestion process may support file-based ingestionfrom files in text formats like Comma-Separated Values (CSV), JavaScriptObject Notion (JSON) and the like. The batch ingestion process may alsosupport ingestion of asset information 211 from semi-structured machinelog formats.

In an embodiment, the receiving module 219 may be further configured forperforming one or more data cleansing operations on the received assetinformation 211 for eliminating one or more irregularities in the assetinformation 211. As an example, the one or more data cleansingoperations may include multiple activities such as filtering out invalidor erroneous data from the asset information 211, replacing missingvalues, de-duplication of the asset information 211 and the like. Oncethe asset information 211 is converted into meaningful/usefulinformation, the receiving module 219 may determine if the receivedasset information 211 is sufficient for predicting the failure in theone or more assets 101. The sufficiency of the asset information 211 maybe determined, using the asset metadata 213 stored in the metadata storeand list of requisite variables based on the asset failure mode fromfailure mode determination module 223. Firstly, the receiving module 219may process the asset information 211 to extract the asset identifiercorresponding to each of the one or more assets 101 from the assetinformation 211. Subsequently, the extracted asset identifier may becompared with the list of asset identifiers and other asset metadata 213stored in metadata store for identifying the asset type of each of theone or more assets 101. Thereafter, a list of data variables,corresponding to each of the one or more assets 101, that are requiredto be derived for predicting the failures in each of the one or moreassets 101 may be derived from the asset metadata 213. Finally, the datain the asset information 211 may be mapped with each of the datavariables to determine sufficiency of the asset information 211.

The process of receiving the asset information 211 and determiningsufficiency of the asset information 211 is illustrated in flowchart ofFIG. 3A. At step 301, each of the one or more data sources 103 may beconfigured for publishing and/or transmitting the asset information 211to the failure prediction system 105. At step 303, the failureprediction system 105 may receive the asset information 211 using atleast one of the batch ingestion process (step 305A) or the real-timestreaming process (step 305B). At step 307, one or more data cleansingoperations may be performed on the received asset information 211.Subsequently, at step 309, the sufficiency of the asset information 211may be determined using the process illustrated in the abovedescription. If the asset information 211 is determined to besufficient, then the asset information 211 may be forwarded for furtherprocessing at step 311A. However, if the asset information 211 isdetermined to be insufficient, the asset information 211 may bediscarded at step 311B. Thereafter, the failure prediction system 105may receive fresh asset information 211 from the one or more datasources 103, until sufficient asset information 211 is collected forpredicting the failures in the one or more assets 101.

In an embodiment, the asset type determination module 221 may beconfigured for determining the asset type of each of the one or moreassets 101 using the asset metadata 213 stored in the metadata store. Inan embodiment, the asset type of each of the one or more assets 101 maybe determined by comparing the asset identifier of each of the one ormore assets 101 with the list of asset identifiers comprised in theasset metadata 213. Similarly, the failure mode determination module 223may be used for determining the failure mode of each of the one or moreassets 101 by comparing the asset information 211 and the asset metadata213.

In an embodiment, the model selection module 225 may be used forselecting one of the plurality of prediction models 107 for predictingfailures in each of the one or more assets 101. In other words, themodel selection module 225 may be used for identifying a correct modelrequired for predicting failures in the one or more assets 101 based onthe asset type and the failure mode of the one or more assets 101. In anembodiment, the model selection module 225 may compare the asset typeand the failure mode of each of the one or more assets 101 with apredetermined asset type and a predetermined failure mode used fortraining each of the plurality of prediction models 107 for selectingthe right model from the plurality of prediction models 107.

In an embodiment, the plurality of prediction models 107 may be used foranalysing the asset information 211 related to the one or more assets101 and predict the failures in the one or more assets 101. In someimplementations, each of the plurality of prediction models 107 may bestored in a model repository associated with the failure predictionsystem 105. The model repository may enlist the plurality of predictionmodels 107 available in the failure prediction system 105, along withinformation such as the asset type and the failure modes used fortraining each of the plurality of prediction models 107. In anembodiment, the plurality of prediction models 107 may be created and/orbuilt using a model building approach. During model building, each ofthe plurality of prediction models 107 are trained using analytical andmachine learning techniques that are trained for different asset typesand failure modes. Further, the analysis results such as significantvariables, model factors and failure propensity scores, generated byeach of the plurality of prediction models 107 may be stored separatelyin a memory portion called ‘results zone’, within the memory 205 of thefailure prediction system 105, as a reference for real-time predictionof the failures.

In an embodiment, the model repository may include multiple predictionmodels 107 for predicting failures in a particular asset type fordifferent failure modes. Therefore, both the asset type and the failuremode of each of the one or more assets 101 may be used for selecting theright prediction model from the model repository. Once the rightprediction model is selected, the selected prediction model may be movedto an active memory of the failure prediction system 105 for analysingincoming asset information 211 in real-time. Further, the resultsgenerated by the selected prediction model may be stored back on themodel repository, which in turn may be used to score the selectedprediction model based on the accuracy of the predictions made by theselected prediction model.

In an embodiment, the level of accuracy of predictions made by each ofthe plurality of prediction models 107 may be analysed periodically forevaluating performance of each of the plurality of prediction models107. Suppose, if the prediction accuracy of the prediction models 107 isbelow a predetermined threshold value, then such prediction models 107may be re-trained with relevant training data for improving theprediction accuracy. Subsequently, the model repository may be updatedwith the re-trained prediction model having improved predictionaccuracy. The above process of analysing the prediction accuracy of theprediction models 107, re-training and/or re-modelling of the predictionmodels 107 may be carried out as a part of model management process. Themodel management process may be performed at regular periodicalintervals to monitor and improvise prediction results of the pluralityof prediction models 107. Additionally, the model management process mayalso include constructing new prediction models 107 when the relevantprediction models 107 for prediction failures in the one or more assets101 having specific asset type and failure mode are not available in themodel repository. Subsequent to constructing the new prediction models107, the model repository may be updated with the new prediction models107 and the prediction accuracy of the new prediction models 107.

FIG. 3B and FIG. 3C illustrate flowcharts that summarize the modelbuilding and model management processes involved in the modelrepository. As shown in FIG. 3B, at step 321, the model repository maybe checked to determine if there exist any relevant prediction modelsfor analysing the asset information 211. If a relevant prediction modelis identified, then at step 323A, the identified relevant predictionmodel may be selected for predicting failures in the asset. Further, atstep 325, the results of prediction generated by the selectedpredication model may be stored in the results zone of the modelrepository and subsequently transmitted to the concerned personnel viaappropriate notification events. On the other hand, if the modelrepository does not have a relevant prediction model, a new predictionmodel may be dynamically created at step 323B. Thereafter, at step 327,each of the prediction models stored in the model repository, includingthe newly created prediction model, may be periodically trained andupdated using a model management process. The model management processis illustrated in FIG. 3C.

As shown in FIG. 3C, at step 329, the results of prediction generated bythe selected prediction model may be retrieved from the results zone andanalysed for determining prediction accuracy of the selected predictionmodel, at step 331. If the prediction accuracy is more than thepredetermined threshold, then the prediction model may not be subjectedfor further training. However, if the prediction accuracy is less thanthe predetermined threshold, then, at step 333B, the selected predictionmodel may be re-trained to improvise the prediction accuracy of theselected prediction model. Further, at step 335, the model repositorymay be updated with the re-trained prediction model. Thereafter, theupdated model repository may be used for predicting failures in the oneor more assets 101 in future instances.

FIG. 4 shows a flowchart illustrating a method of predicting failures ina diverse set of asset types in an enterprise in accordance with someembodiments of the present disclosure.

As illustrated in FIG. 4, the method 400 may include one or more blocksillustrating a method of predicting failures in a diverse set of assettypes in an enterprise using the failure prediction system 105illustrated in FIG. 1. The method 400 may be described in the generalcontext of computer executable instructions. Generally, computerexecutable instructions can include routines, programs, objects,components, data structures, procedures, modules, and functions, whichperform specific functions or implement specific abstract data types.

The order in which the method 400 is described is not intended to beconstrued as a limitation, and any number of the described method blockscan be combined in any order to implement the method. Additionally,individual blocks may be deleted from the methods without departing fromthe scope of the subject matter described herein. Furthermore, themethod can be implemented in any suitable hardware, software, firmware,or combination thereof.

At block 401, the method 400 includes receiving, by the failureprediction system 105, asset information 211 related to one or moreassets 101 from one or more data sources 103 associated with the one ormore assets 101. The one or more assets 101 belong to one or more assettypes. As an example, the asset information 211 may include, withoutlimiting to, at least one of an asset identifier corresponding to eachof the one or more assets 101, values of operating parameters of the oneor more assets 101, values of operating parameters of an ambientenvironment of the one or more assets 101, events indicating changes inoperational states of the one or more assets 101 and alarms indicatingvariations in operation of the one or more assets 101. Further, the oneor more data sources 103 may include, without limiting to, one or moresensors configured with the one or more assets 101 and data logsindicating operation, maintenance and servicing of the one or moreassets 101. In an embodiment, the asset information 211 may be receivedin real-time or at predetermined periodical intervals.

At block 403, the method 400 includes determining, by the failureprediction system 105, an asset type and a failure mode of each of theone or more assets 101 based on analysis of the asset information 211.In an embodiment, determining the asset type and the failure mode ofeach of the one or more assets 101 includes determining an assetidentifier corresponding to the each of the one or more assets 101 usingthe asset information 211. Further, the method includes determining theasset type and the failure mode of each of the one or more assets 101based on the asset identifier and asset metadata 213 stored in ametadata store associated with the failure prediction system 105. Themethod includes determining sufficiency of the asset information 211based on comparison of the asset information 211 with one or morepredetermined asset parameters. In some implementations, receiving theasset information 211 includes performing one or more data cleansingoperations on the asset information 211 for eliminating one or moreirregularities in the asset information 211. In an embodiment, each ofthe one or more asset types may be associated with a plurality ofprediction models 107 and each of the plurality of prediction models 107may be associated with a predetermined failure mode.

At block 405, the method 400 includes selecting, by the failureprediction system 105, one of a plurality of prediction models 107 forpredicting failures in each of the one or more assets 101 based on theasset type and the failure mode of each of the one or more assets 101.In an embodiment, selecting the one of the plurality of predictionmodels 107 includes comparing the asset type and the failure mode ofeach of the one or more assets 101 with pretrained asset type andpretrained failure mode used for training each of the plurality ofprediction models 107. Further, the method includes selecting one of theplurality of prediction models 107 for each of the one or more assets101 based on the comparison.

At block 407, the method 400 includes analyzing, by the failureprediction system 105, the asset information 211 using selected one ofthe plurality of prediction models 107 for predicting the failures ineach of the one or more assets 101. In an embodiment, when the assettype and the failure mode of the one or more assets 101 do not matchwith the pretrained asset type and the pretrained failure mode of theplurality of prediction models 107, new prediction models 107 may bedynamically created for predicting failures in the one or more assets101. Further, each of the new prediction models 107 may be stored in amodel repository associated with the failure prediction system 105 forsubsequent prediction of failures in the one or more assets 101. In anembodiment, the method 400 also includes training the plurality ofprediction models 107 for predicting failures in the one or more assets101 when prediction accuracy level of the plurality of prediction models107 is less than a predetermined threshold.

Computer System

FIG. 5 illustrates a block diagram of an exemplary computer system 500for implementing embodiments consistent with the present disclosure. Inan embodiment, the computer system 500 may be the failure predictionsystem 105 illustrated in FIG. 1, which may be used for predictingfailures in a diverse set of asset types in an enterprise. The computersystem 500 may include a central processing unit (“CPU” or “processor”)502. The processor 502 may comprise at least one data processor forexecuting program components for executing user- or system-generatedbusiness processes. A user may include a person, an assetmanager/supervisor, or any system/sub-system being operated parallellyto the computer system 500. The processor 502 may include specializedprocessing units such as integrated system (bus) controllers, memorymanagement control units, floating point units, graphics processingunits, digital signal processing units, etc.

The processor 502 may be disposed in communication with one or moreinput/output (I/O) devices (511 and 512) via I/O interface 501. The I/Ointerface 501 may employ communication protocols/methods such as,without limitation, audio, analog, digital, stereo, IEEE®-1394, serialbus, Universal Serial Bus (USB), infrared, PS/2, BNC, coaxial,component, composite, Digital Visual Interface (DVI), high-definitionmultimedia interface (HDMI), Radio Frequency (RF) antennas, S-Video,Video Graphics Array (VGA), IEEE® 802.n/b/g/n/x, Bluetooth, cellular(e.g., Code-Division Multiple Access (CDMA), High-Speed Packet Access(HSPA+), Global System For Mobile Communications (GSM), Long-TermEvolution (LTE) or the like), etc. Using the I/O interface 501, thecomputer system 500 may communicate with one or more I/O devices 511 and512.

In some embodiments, the processor 502 may be disposed in communicationwith a communication network 509 via a network interface 503. Thenetwork interface 503 may communicate with the communication network509. The network interface 503 may employ connection protocolsincluding, without limitation, direct connect, Ethernet (e.g., twistedpair 10/100/1000 Base T), Transmission Control Protocol/InternetProtocol (TCP/IP), token ring, IEEE® 802.11a/b/g/n/x, etc. Using thenetwork interface 503 and the communication network 509, the computersystem 500 may communicate with one or more data sources 103 associatedwith one or more assets 101 for receiving asset information 211 relatedto the one or more assets 101 from one or more data sources 103associated with the one or more assets 101.

In an implementation, the communication network 509 may be implementedas one of the several types of networks, such as intranet or Local AreaNetwork (LAN) and such within the organization. The communicationnetwork 509 may either be a dedicated network or a shared network, whichrepresents an association of several types of networks that use avariety of protocols, for example, Hypertext Transfer Protocol (HTTP),Transmission Control Protocol/Internet Protocol (TCP/IP), WirelessApplication Protocol (WAP), etc., to communicate with each other.Further, the communication network 509 may include a variety of networkdevices, including routers, bridges, servers, computing devices, storagedevices, etc.

In some embodiments, the processor 502 may be disposed in communicationwith a memory 505 (e.g., RAM 513, ROM 514, etc. as shown in FIG. 5) viaa storage interface 504. The storage interface 504 may connect to memory505 including, without limitation, memory drives, removable disc drives,etc., employing connection protocols such as Serial Advanced TechnologyAttachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394,Universal Serial Bus (USB), fiber channel, Small Computer SystemsInterface (SCSI), etc. The memory drives may further include a drum,magnetic disc drive, magneto-optical drive, optical drive, RedundantArray of Independent Discs (RAID), solid-state memory devices,solid-state drives, etc.

The memory 505 may store a collection of program or database components,including, without limitation, user/application interface 506, anoperating system 507, a web browser 508, and the like. In someembodiments, computer system 500 may store user/application data 506,such as the data, variables, records, etc. as described in thisinvention. Such databases may be implemented as fault-tolerant,relational, scalable, secure databases such as Oracle® or Sybase®.

The operating system 507 may facilitate resource management andoperation of the computer system 500. Examples of operating systemsinclude, without limitation, APPLE® MACINTOSH® OS X®, UNIX®, UNIX-likesystem distributions (E.G., BERKELEY SOFTWARE DISTRIBUTION® (BSD),FREEBSD®, NETBSD®, OPENBSD, etc.), LINUX® DISTRIBUTIONS (E.G., RED HAT®,UBUNTU®, KUBUNTU®, etc.), IBM® OS/2®, MICROSOFT® WINDOWS® (XP®,VISTA®/7/8, 10 etc.), APPLE® IOS®, GOOGLE™ ANDROID™, BLACKBERRY® OS, orthe like.

The user interface 506 may facilitate display, execution, interaction,manipulation, or operation of program components through textual orgraphical facilities. For example, the user interface 506 may providecomputer interaction interface elements on a display system operativelyconnected to the computer system 500, such as cursors, icons, checkboxes, menus, scrollers, windows, widgets, and the like. Further,Graphical User Interfaces (GUIs) may be employed, including, withoutlimitation, APPLE® MACINTOSH® operating systems' Aqua®, IBM® OS/2®,MICROSOFT® WINDOWS® (e.g., Aero, Metro, etc.), web interface libraries(e.g., ActiveX, JAVA®, JAVASCRIPT®, AJAX, HTML, ADOBE® FLASH®, etc.), orthe like.

The web browser 508 may be a hypertext viewing application. Secure webbrowsing may be provided using Secure Hypertext Transport Protocol(HTTPS), Secure Sockets Layer (SSL), Transport Layer Security (TLS), andthe like. The web browsers 508 may utilize facilities such as AJAX,DHTML, ADOBE® FLASH®, JAVASCRIPT®, JAVA®, Application ProgrammingInterfaces (APIs), and the like. Further, the computer system 500 mayimplement a mail server stored program component. The mail server mayutilize facilities such as ASP, ACTIVEX®, ANSI® C++/C#, MICROSOFT®,.NET, CGI SCRIPTS, JAVA®, JAVASCRIPT®, PERL®, PHP, PYTHON®, WEBOBJECTS®,etc. The mail server may utilize communication protocols such asInternet Message Access Protocol (IMAP), Messaging ApplicationProgramming Interface (MAPI), MICROSOFT® exchange, Post Office Protocol(POP), Simple Mail Transfer Protocol (SMTP), or the like. In someembodiments, the computer system 500 may implement a mail client storedprogram component. The mail client may be a mail viewing application,such as APPLE® MAIL, MICROSOFT® ENTOURAGE®, MICROSOFT® OUTLOOK®,MOZILLA® THUNDERBIRD®, and the like.

Furthermore, one or more computer-readable storage media may be utilizedin implementing embodiments consistent with the present invention. Acomputer-readable storage medium refers to any type of physical memoryon which information or data readable by a processor may be stored.Thus, a computer-readable storage medium may store instructions forexecution by one or more processors, including instructions for causingthe processor(s) to perform steps or stages consistent with theembodiments described herein. The term “computer-readable medium” shouldbe understood to include tangible items and exclude carrier waves andtransient signals, i.e., non-transitory. Examples include Random AccessMemory (RAM), Read-Only Memory (ROM), volatile memory, nonvolatilememory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs),flash drives, disks, and any other known physical storage media.

Advantages of the embodiments of the present disclosure are illustratedherein.

In an embodiment, the method of present disclosure helps in predictingfailures in a diverse set of asset types in an enterprise.

In an embodiment, the failure prediction system of present disclosuremay function as a universal framework for predicting failures in adiverse set of asset types. Thus, the failure prediction system ofpresent disclosure helps to avoid expenditure involved in deployingmultiple failure prediction systems that are specific to only aparticular type of assets.

In an embodiment, the method of present disclosure provides a proactivemodeling approach for dynamically predicting failures in the diverse setof asset types.

The terms “an embodiment”, “embodiment”, “embodiments”, “theembodiment”, “the embodiments”, “one or more embodiments”, “someembodiments”, and “one embodiment” mean “one or more (but not all)embodiments of the invention(s)” unless expressly specified otherwise.

The terms “including”, “comprising”, “having” and variations thereofmean “including but not limited to”, unless expressly specifiedotherwise.

The enumerated listing of items does not imply that any or all the itemsare mutually exclusive, unless expressly specified otherwise. The terms“a”, “an” and “the” mean “one or more”, unless expressly specifiedotherwise.

A description of an embodiment with several components in communicationwith each other does not imply that all such components are required. Onthe contrary, a variety of optional components are described toillustrate the wide variety of possible embodiments of the invention.

When a single device or article is described herein, it will be clearthat more than one device/article (whether they cooperate) may be usedin place of a single device/article. Similarly, where more than onedevice or article is described herein (whether they cooperate), it willbe clear that a single device/article may be used in place of the morethan one device or article or a different number of devices/articles maybe used instead of the shown number of devices or programs. Thefunctionality and/or the features of a device may be alternativelyembodied by one or more other devices which are not explicitly describedas having such functionality/features. Thus, other embodiments of theinvention need not include the device itself.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the invention be limited notby this detailed description, but rather by any claims that issue on anapplication based here on. Accordingly, the embodiments of the presentinvention are intended to be illustrative, but not limiting, of thescope of the invention, which is set forth in the following claims.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

What is claimed is:
 1. A method of predicting failures in a diverse setof asset types in an enterprise, the method comprising: receiving, by afailure prediction system, asset information related to one or moreassets from one or more data sources associated with the one or moreassets, wherein the one or more assets belong to one or more assettypes; determining, by the failure prediction system, an asset type anda failure mode of each of the one or more assets based on analysis ofthe asset information; selecting, by the failure prediction system, oneof a plurality of prediction models for predicting failures in each ofthe one or more assets based on the asset type and the failure mode ofeach of the one or more assets; and analysing, by the failure predictionsystem, the asset information using selected one of the plurality ofprediction models for predicting the failures in each of the one or moreassets.
 2. The method as claimed in claim 1, wherein the assetinformation comprises at least one of an asset identifier correspondingto each of the one or more assets, values of operating parameters of theone or more assets, values of operating parameters of an ambientenvironment of the one or more assets, events indicating changes inoperational states of the one or more assets or alarms indicatingvariations in operation of the one or more assets.
 3. The method asclaimed in claim 1, wherein the asset information is received inreal-time or at predetermined periodical intervals.
 4. The method asclaimed in claim 1, wherein the one or more data sources comprises oneor more sensors configured with the one or more assets and data logsindicating operation, maintenance and servicing of the one or moreassets.
 5. The method as claimed in claim 1, wherein receiving the assetinformation further comprises: performing one or more data cleansingoperations on the asset information for eliminating one or moreirregularities in the asset information; and determining sufficiency ofthe asset information based on comparison of the asset information withone or more predetermined asset parameters.
 6. The method as claimed inclaim 1, wherein determining the asset type and the failure mode of eachof the one or more assets comprises: determining an asset identifiercorresponding to the each of the one or more assets using the assetinformation; and determining the asset type and the failure mode of eachof the one or more assets based on the asset identifier and assetmetadata stored in a metadata store associated with the failureprediction system.
 7. The method as claimed in claim 1, whereinselecting the one of the plurality of prediction models for predictingfailures in each of the one or more assets comprises: comparing theasset type and the failure mode of each of the one or more assets withpretrained asset type and pretrained failure mode used for training eachof the plurality of prediction models; and selecting one of theplurality of prediction models for each of the one or more assets basedon comparison.
 8. The method as claimed in claim 7 further comprisesdynamically creating new prediction models for predicting failures inthe one or more assets when the asset type and the failure mode of theone or more assets do not match with the pretrained asset type and thepretrained failure mode of the plurality of prediction models, whereineach of the new prediction models are stored in a model repositoryassociated with the failure prediction system for subsequent predictionof failures in the one or more assets.
 9. The method as claimed in claim1 further comprises training the plurality of prediction models forpredicting failures in the one or more assets when prediction accuracylevel of the plurality of prediction models is less than a predeterminedthreshold.
 10. The method as claimed in claim 1 further comprisesgenerating and transmitting one or more notification events to assetmanagement personnel, associated with the one or more assets, uponpredicting failures in the one or more assets.
 11. The method as claimedin claim 1, wherein each of the one or more asset types is associatedwith a plurality of prediction models and each of the plurality ofprediction models is associated with a predetermined failure mode.
 12. Afailure prediction system for predicting failures in a diverse set ofasset types in an enterprise, the failure prediction system comprising:a processor; and a memory, communicatively coupled to the processor,wherein the memory stores processor-executable instructions, which onexecution, cause the processor to: receive asset information related toone or more assets from one or more data sources associated with the oneor more assets, wherein the one or more assets belong to one or moreasset types; determine an asset type and a failure mode of each of theone or more assets based on analysis of the asset information; selectone of a plurality of prediction models to predict failures in each ofthe one or more assets based on the asset type and the failure mode ofeach of the one or more assets; and analyse the asset information usingselected one of the plurality of prediction models to predict thefailures in each of the one or more assets.
 13. The failure predictionsystem as claimed in claim 12, wherein the asset information comprisesat least one of an asset identifier corresponding to each of the one ormore assets, values of operating parameters of the one or more assets,values of operating parameters of an ambient environment of the one ormore assets, events indicating changes in operational states of the oneor more assets or alarms indicating variations in operation of the oneor more assets.
 14. The failure prediction system as claimed in claim12, wherein the processor receives the asset information in real-time orat predetermined periodical intervals.
 15. The failure prediction systemas claimed in claim 12, wherein the one or more data sources comprisesone or more sensors configured with the one or more assets and data logsindicating operation, maintenance and servicing of the one or moreassets.
 16. The failure prediction system as claimed in claim 12,wherein the processor is further configured to: perform one or more datacleansing operations on the asset information to eliminate one or moreirregularities in the asset information; and determine sufficiency ofthe asset information based on comparison of the asset information withone or more predetermined asset parameters.
 17. The failure predictionsystem as claimed in claim 12, wherein to determine the asset type ofeach of the one or more assets, the processor is configured to:determine an asset identifier corresponding to the each of the one ormore assets using the asset information; and determine the asset typeand the failure mode of each of the one or more assets based on theasset identifier and asset metadata stored in a metadata storeassociated with the failure prediction system.
 18. The failureprediction system as claimed in claim 12, wherein to select the one ofthe plurality of prediction models for predicting failures in each ofthe one or more assets, the processor is configured to: compare theasset type and the failure mode of each of the one or more assets withpretrained asset type and pretrained failure mode used for training eachof the plurality of prediction models; and select one of the pluralityof prediction models for each of the one or more assets based oncomparison.
 19. The failure prediction system as claimed in claim 18,wherein the processor is configured to dynamically crate new predictionmodels for predicting failures in the one or more assets when the assettype and the failure mode of the one or more assets do not match withthe pretrained asset type and the pretrained failure mode of theplurality of prediction models, wherein the processor stores each of thenew prediction models in a model repository associated with the failureprediction system for subsequent prediction of failures in the one ormore assets.
 20. The failure prediction system as claimed in claim 12,wherein the processor is configured to train the plurality of predictionmodels for predicting failures in the one or more assets when predictionaccuracy level of the plurality of prediction models is less than apredetermined threshold.
 21. The failure prediction system as claimed inclaim 12, wherein the processor is configured to generate and transmitone or more notification events to asset management personnel,associated with the one or more assets, upon predicting failures in theone or more assets.
 22. The failure prediction system as claimed inclaim 12, wherein the processor associates each of the one or more assettypes with a plurality of prediction models and each of the plurality ofprediction models with a predetermined failure mode.